#randomwalk — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #randomwalk, aggregated by home.social.
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The #UselessGraphic of the day is a #randomwalk. You can read the code and try it at home, with some beer at hand.
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The #UselessGraphic of the day is a #randomwalk. You can read the code and try it at home, with some beer at hand.
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A Random Walk in 10 Dimensions (2021)
https://galileo-unbound.blog/2021/06/28/a-random-walk-in-10-dimensions/
#HackerNews #A #Random #Walk #in #10 #Dimensions #2021 #RandomWalk #Dimensions #Mathematics #Exploration
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A Random Walk in 10 Dimensions (2021)
https://galileo-unbound.blog/2021/06/28/a-random-walk-in-10-dimensions/
#HackerNews #A #Random #Walk #in #10 #Dimensions #2021 #RandomWalk #Dimensions #Mathematics #Exploration
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`His initial intended uses were for linguistic analysis and other mathematical subjects like card shuffling, but both Markov chains and matrices rapidly found use in other fields.`
https://en.wikipedia.org/wiki/Stochastic_matrix#History
#AndreyMarkov #Markov #MarkovChain #MarkovModel #statistics #stochastic #stochasticProcess #randomWalk #statisticalPhysics #physics #inference #distribution #equilibrium #transitionRate #transitionMatrix #MarkovMatrix #stochasticMatrix #linearAlgebra #differentialEquation #equation
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`His initial intended uses were for linguistic analysis and other mathematical subjects like card shuffling, but both Markov chains and matrices rapidly found use in other fields.`
https://en.wikipedia.org/wiki/Stochastic_matrix#History
#AndreyMarkov #Markov #MarkovChain #MarkovModel #statistics #stochastic #stochasticProcess #randomWalk #statisticalPhysics #physics #inference #distribution #equilibrium #transitionRate #transitionMatrix #MarkovMatrix #stochasticMatrix #linearAlgebra #differentialEquation #equation
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Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Lognormal #Distribution with random_lognormal_walk() See below :) #ggplot2 #tidydata
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Just updated my #R #Package in #dev on #GitHub with another #RandomWalk this time from the #Logistic #Distribution with random_logistic_walk() See below :) #ggplot2 #tidydata
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How about a #3D #Plot using #Plotly for a #NegativeBinomial #RandomWalk
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Another new #RandomWalk function for my #R #Package #RandomWalker this time for a #Discrete #Distribution of the Negative Binomial. This is in Dev. #RandomWalk #Random #R #Rcode #RProgramming #ggplot2 #tidydata
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Want to visualize a 2D Random Walk of the Wilcox distribution in #R then the RandomWalker package developed by myself and Antti Rask can do that. #R #RStats #RandomWalker #RandomWalk #Visualization #ggplot2 #TidyData
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Hexagonaler Random Walk mit TigerJython
Der bei Microsoft forschende Psychologe und Informatiker Dan Goldstein berichtet in seinem Blog, wie er mit seiner neunjährigen Tochter in einem Bagel-Shop warten mußte. Um sich die Langeweile zu verkürzen, kramte er ein hexagonal-kariertes Blatt Papier und einen Würfel hervor (Psychologen bei Microsoft haben immer ein hexagonal-karierten Notizblock und einen Würfel in der Tasche). https://kantel.github.io/posts/2025061802_hexawalk/ #TigerJython #RandomWalk #Python #CreativeCoding
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Hexagonaler Random Walk mit TigerJython
Der bei Microsoft forschende Psychologe und Informatiker Dan Goldstein berichtet in seinem Blog, wie er mit seiner neunjährigen Tochter in einem Bagel-Shop warten mußte. Um sich die Langeweile zu verkürzen, kramte er ein hexagonal-kariertes Blatt Papier und einen Würfel hervor (Psychologen bei Microsoft haben immer ein hexagonal-karierten Notizblock und einen Würfel in der Tasche). https://kantel.github.io/posts/2025061802_hexawalk/ #TigerJython #RandomWalk #Python #CreativeCoding
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#genuary17 - Pi is 4? When contemplating how to approach this one, I recalled the fascinating Monte Carlo methods for calculating pi, like counting how many random dots fall inside a circle, or Buffon's needle experiment. A method unfamiliar to me until now method is based on random walk. I had to code it up and try it, and then thought about what would have to change to make pi come out to be 4. Instead of a random walk where each step is randomly either -1 or 1, the steps would need to be approximately -0.885 or +0.885. This image visualizes the difference, with the cream color walks for the regular pi, and the turquoise paths the "pi=4". #pi #randomwalk #genuary #genuary2025
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#genuary17 - Pi is 4? When contemplating how to approach this one, I recalled the fascinating Monte Carlo methods for calculating pi, like counting how many random dots fall inside a circle, or Buffon's needle experiment. A method unfamiliar to me until now method is based on random walk. I had to code it up and try it, and then thought about what would have to change to make pi come out to be 4. Instead of a random walk where each step is randomly either -1 or 1, the steps would need to be approximately -0.885 or +0.885. This image visualizes the difference, with the cream color walks for the regular pi, and the turquoise paths the "pi=4". #pi #randomwalk #genuary #genuary2025
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Hot from the press, with Richard Mann
and @elva: 🐜 Random walks with spatial and temporal resets can explain individual and colony-level searching patterns in antshttps://royalsocietypublishing.org/doi/10.1098/rsif.2024.0149
#physics #ants #animalmovement #animabehaviour #randomwalk #behaviouralecology
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Hot from the press, with Richard Mann
and @elva: 🐜 Random walks with spatial and temporal resets can explain individual and colony-level searching patterns in antshttps://royalsocietypublishing.org/doi/10.1098/rsif.2024.0149
#physics #ants #animalmovement #animabehaviour #randomwalk #behaviouralecology
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#Day29 of the #30DayChartChallenge, #black_n_white
🚶🏻Random walk simulation
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#Day29 of the #30DayChartChallenge, #black_n_white
🚶🏻Random walk simulation
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A drunk man will find his way home, but a drunk bird may get lost forever.” What is this sentence about?
In 2D, the random walk is “recurrent”, i.e. you are guaranteed to go back to where you started; but in 3D, the random walk is “transient”, the opposite of “recurrent”. In fact, for the 2D case, that also means that you are guaranteed to go to ALL places in the world (the only constraint is, of course, time). [Think about why.]
Markov chains are also an important tool in modelling the real world, and so I feel like this is a good excuse for bringing it up.
At the end, I also compare this phenomenon to Stein’s paradox – in both cases, there is a cutoff between 2 and 3 dimensions, and they have similar intuitive explanation – is that a coincidence?
Random walks in 2D and 3D are fundamentally different
#MarkovChain #MarkovChains #Math #Mathematics #nowWatching #randomWalk #randomness #StochasticProcess #YouTube
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Hexagonaler Random Walker (mit der Turtle und in Trinket)
Da ich mit meinen Spiralen Blut geleckt hatte, wollte ich unbedingt zu Beginn des Jahres noch etwas mit Pythons Turtle anstellen. Also habe ich den hexagonalen Random Walk, den ich im Dezember 2017 schon einmal hier in diesem Blog Kritzelheft vorgestellt hatte, wieder aus der Mottenkiste hervorgekramt und ein wenig aufgehübscht. https://kantel.github.io/posts/2024010501_hex_rand_walk/ #Python #Turtle #CreativeCoding #RandomWalk #Trinket
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Hexagonaler Random Walker (mit der Turtle und in Trinket)
Da ich mit meinen Spiralen Blut geleckt hatte, wollte ich unbedingt zu Beginn des Jahres noch etwas mit Pythons Turtle anstellen. Also habe ich den hexagonalen Random Walk, den ich im Dezember 2017 schon einmal hier in diesem Blog Kritzelheft vorgestellt hatte, wieder aus der Mottenkiste hervorgekramt und ein wenig aufgehübscht. https://kantel.github.io/posts/2024010501_hex_rand_walk/ #Python #Turtle #CreativeCoding #RandomWalk #Trinket
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Then you can plot it with:
```import matplotlib.pyplot as plt
ax = plt.figure().add_subplot(projection='3d')
ax.plot(*w.T, lw=0.25, alpha=0.5)
plt.show()
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TIL that NumPy has `np.cumsum` and `np.cumprod` that you can use to accumulate sums and products, and you can use them in one axis of a multidimensional array.
For instance, you can have a 3xn matrix of 3D vectors centered around (0,0,0)…
```python
import numpy as py
v = np.random.rand(1000,3)*0.1-0.05```You can calculate a random walk with
`w = np.cumsum(v, axis=0)`
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If I do a random walk on the Euclidean plane, and I look at the limiting case of n steps of length 1/n for n->inf , will the propability distribution of the end point be evenly distributed in the unit circle around the starting point?
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If I do a random walk on the Euclidean plane, and I look at the limiting case of n steps of length 1/n for n->inf , will the propability distribution of the end point be evenly distributed in the unit circle around the starting point?
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When you go for a hike but don't plan out a route first. #Oakland #RandomWalk #GoOutside
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#randomwalk #probability It's well known that simple random walk on ℤᵈ is transient iff 𝑑>2. Hence if a drunk man starts a random walk from the origin, the probability \(p_d\) that he eventually returns to origin is strictly less than one. But what's the exact value of \(p_d\)?
Here is a Python script that uses Monte Carlo to calculate \(p_d\); it runs extremely fast: https://gist.github.com/neozhaoliang/5d677cf85bcd356af3a510ef41a2f185
It gives \(p_3\approx0.3405,p_4\approx0.1933,p_{10}\approx0.0562\), very near to the true values!
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#randomwalk #probability It's well known that simple random walk on ℤᵈ is transient iff 𝑑>2. Hence if a drunk man starts a random walk from the origin, the probability \(p_d\) that he eventually returns to origin is strictly less than one. But what's the exact value of \(p_d\)?
Here is a Python script that uses Monte Carlo to calculate \(p_d\); it runs extremely fast: https://gist.github.com/neozhaoliang/5d677cf85bcd356af3a510ef41a2f185
It gives \(p_3\approx0.3405,p_4\approx0.1933,p_{10}\approx0.0562\), very near to the true values!
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If you choose a perfectly random spanning tree of a 𝑛×𝑛 grid (a maze), then as 𝑛→∞ , the distribution of vertex degree will approach limit:
1. Dead ends: \(P(d=1)=\dfrac{8}{\pi^2}(1-\dfrac{2}{\pi})\approx 0.294\)
2. Straights/Turns: \(P(d=2)=\dfrac{4}{\pi}(2-\dfrac{9}{\pi}+\dfrac{12}{\pi^2})\approx0.447\)
3. Forks: \(P(d=3)=2(1-\dfrac{2}{\pi})(1-\dfrac{6}{\pi}+\dfrac{12}{\pi^2})\approx 0.222\)
4. Crosses: \(P(d=4)=(\dfrac{4}{\pi}-1)(1-\dfrac{2}{\pi})^2\approx 0.036\)
#probability #randomwalk -
Spaß mit der Turtle: Random Walk mit Processing.py
Ich wollte mit der Turtle-Bibliothek für Processing(.py) einfach etwas völlig Sinnbefreites anstellen und so habe ich vier Schildkröten auf eine zufallsgesteuerte Wanderschaft geschickt (Random Walk). Außerdem sollte das Fenster knallbunt werden. Auf der Homepage des Turtle-Moduls fand ich einen Screenshot, dessen Farben mir bunt genug schienen. http://blog.schockwellenreiter.de/2020/12/2020122901.html #Python #Processing #ProcessingPy #CreativeCoding #Turtle #RandomWalk
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Random Walk mit der Python Arcade-Bibliothek
Daniel Shiffmans Buch »The Nature of Code« hat auch noch ein Kapitel 0. In diesem einführenden Kapitel geht es unter anderem um Zufallszahlen und Random Walk. Solch einen (zweidimensionalen) #RandomWalk wollte ich natürlich ebenfalls nach #Python portieren, auch mit Vektoren umgesetzt und ich wollte das auch mit der Python #Arcade-Bibliothek umsetzen. http://blog.schockwellenreiter.de/2020/06/2020063002.html #NatureOfCode
